500 Healthy Cities GIS Assignment

Load the libraries and set the working directory

library(tidyverse)
library(plotly)
library(leaflet)
library(ggrepel)
library(ggthemes)
library(viridis)
setwd("/Users/nhi.vu/Desktop/DATA110")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")
data(cities500)

The GeoLocation variable has (lat, long) format

Split GeoLocation (lat, long) into two columns: lat and long

latlong <- cities500|>
  mutate(GeoLocation = str_replace_all(GeoLocation, "[()]", ""))|>
  separate(GeoLocation, into = c("lat", "long"), sep = ",", convert = TRUE)
head(latlong)

# A tibble: 6 × 25
   Year StateAbbr StateDesc  CityName  GeographicLevel DataSource Category      
  <dbl> <chr>     <chr>      <chr>     <chr>           <chr>      <chr>         
1  2017 CA        California Hawthorne Census Tract    BRFSS      Health Outcom…
2  2017 CA        California Hawthorne City            BRFSS      Unhealthy Beh…
3  2017 CA        California Hayward   City            BRFSS      Health Outcom…
4  2017 CA        California Hayward   City            BRFSS      Unhealthy Beh…
5  2017 CA        California Hemet     City            BRFSS      Prevention    
6  2017 CA        California Indio     Census Tract    BRFSS      Health Outcom…
# ℹ 18 more variables: UniqueID <chr>, Measure <chr>, Data_Value_Unit <chr>,
#   DataValueTypeID <chr>, Data_Value_Type <chr>, Data_Value <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
#   Data_Value_Footnote_Symbol <chr>, Data_Value_Footnote <chr>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

For your assignment, work with a cleaned dataset.

1. Once you run the above code and learn how to filter in this format, filter this dataset however you choose so that you have a subset with no more than 900 observations.

Filter chunk here

obesity1 <- latlong |>
  filter(StateAbbr == "CA") |>
  filter(Data_Value_Type == "Age-adjusted prevalence") |>
  filter(Category == "Unhealthy Behaviors") |>
  filter(Short_Question_Text %in% c("Obesity", "Binge Drinking", "Current Smoking")) |>
  filter(Year == 2017) |>
  select (Year, StateAbbr, CityName, PopulationCount, Data_Value, Short_Question_Text, lat, long)
head(obesity1)

# A tibble: 6 × 8
   Year StateAbbr CityName  PopulationCount Data_Value Short_Question_Text   lat
  <dbl> <chr>     <chr>               <dbl>      <dbl> <chr>               <dbl>
1  2017 CA        Indio               76036       17.7 Binge Drinking       33.7
2  2017 CA        Corona             152374       26.8 Obesity              33.9
3  2017 CA        Fullerton          135161       18.4 Binge Drinking       33.9
4  2017 CA        Fullerton          135161       20.5 Obesity              33.9
5  2017 CA        San Diego         1307402       22.4 Obesity              32.8
6  2017 CA        Tracy               82922       20   Binge Drinking       37.7
# ℹ 1 more variable: long <dbl>

top15 <- obesity1 |>
  arrange(desc(PopulationCount)) |>
  head(15)

2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.

First plot chunk here

p1 <- ggplot(top15, aes(x = CityName,
                        y = Data_Value, 
                        fill = Short_Question_Text, 
                        text = paste0("City: ", CityName, # to add text for the tool tips
                                     "<br>Population Count: ", PopulationCount,
                                     "<br>Prevalence (%): ", Data_Value, "%"))) +
  geom_col(position = "dodge") +
  coord_flip()+
  labs(title = "Obesity vs Smoking vs Drinking in Top 5 most populated CA Cities (2017)",
       x = "City",
       y = "Prevalence (%)",
       fill = "Health Behavior") +
  theme_grey()+ 
  scale_fill_wsj() 

p2 <- ggplotly(p1, tooltip = "text") #to make it interactive, can be hover over.
p2

You can hover over the points for exact percentage!

3. Now create a map of your subsetted dataset.

First map chunk here

leaflet() |>
  setView(lng = -119.4, lat = 36.7, zoom = 5.6) |> # Put in the coords for CA to have it set as the view
  addProviderTiles("Esri.WorldStreetMap") |> # this is for the design of the map
  addCircles( 
    data = obesity1, # this is to plot the circles on the map
    radius = obesity1$PopulationCount / 100, 
    color = "green") 

Assuming "long" and "lat" are longitude and latitude, respectively

4. Refine your map to include a mouse-click tooltip

Refined map chunk here

popup1 <- paste0("<b>City: </b>", obesity1$CityName, "<br>", #adding the text for the tool tip
                 "<b>Population: </b>", obesity1$PopulationCount, "<br>",
                 "<b>Unhealthy Behavior: </b>", obesity1$Short_Question_Text, "<br>",
                 "<b>Prevalence Rate: </b>", obesity1$Data_Value, "%", "<br>")

leaflet() |>
  setView(lng = -119.4, lat = 36.7, zoom = 5.6) |> # Put in the coords for CA to have it set as the view
  addProviderTiles("Esri.WorldStreetMap") |> # this is for the design of the map
  addCircles( 
    data = obesity1, # this is to plot the circles on the map
    radius = obesity1$PopulationCount / 100, 
    color = "green",
    popup = popup1) 

Assuming "long" and "lat" are longitude and latitude, respectively

You can know click on each circle to see their information!

Next I’m adding colors for 3 different types of unhealthy behaviors

colors1 <- colorFactor(palette = c("blue", "brown", "deeppink"), levels = c("Binge Drinking", "Current Smoking", "Obesity"), obesity1$Short_Question_Text)

map1 <- leaflet(obesity1) |>
  setView(lng = -119, lat = 36, zoom = 6.2) |>
  addProviderTiles("Esri.WorldStreetMap") |> 
  addCircles(
    fillColor = ~colors1(Short_Question_Text),
    fillOpacity = 1,
    data = obesity1, 
    radius = obesity1$PopulationCount / 100, 
    color =  ~colors1(Short_Question_Text),
    popup = popup1) 

Assuming "long" and "lat" are longitude and latitude, respectively

map1 |>
  addLegend("bottomleft", pal = colors1, values = obesity1$Short_Question_Text,
            title = "Unhealthy Behaviors", opacity = 1)

5. Write a paragraph

In a paragraph, describe the plots you created and what they show.

I first downloaded all of the library packages that I needed. I then cleaned and filtered the data set to only use the information I need. For the project, I wanted to make a bar graph of the prevalence rates of smoking, binge drinking, and obesity in CA in 2017. I originally wanted to include the 15 most populated cities in California, but the filtering took me down to the top 5 instead. To improve readability for viewers to read the names of the cities, I utilized coord_flip(). It can be seen from the bar graph, which issue became the most common in most cities, which is obesity. Although in San Francisco, binge drinking was prevalent the most. I added tool tips for the viewer that displayed the city name, population and exact percentage when the user hovers over any of the bars.

Following the bar chart, I created an interactive map of California with the same data set. The map indicates all the cities in CA and which unhealthy behavior is highlighted in each city. I used the instructions that were included in the earthquake mapping tutorial: I set the coordinates, selected a particular map design and placed circle markers for each city. Each circle marker had a tooltip that displayed the city name, population, unhealthy behavior, and the prevalence rate of the particular unhealthy behavior. In addition, I used color coding and included a legend which improved the visualization substantially.

For the mapping graph, we can see again that obesity comes across as a public health issue for a number of the California cities in the data set. Overall, the project illustrates that obesity is pervasive as evidenced by its classification as a public health issue in California.